Optical respiration rate detection device

ABSTRACT

A respiration rate detection device including a photoplethysmography (PPG) detector, an acceleration detector and a processing unit is provided. The processing unit calculates an acceleration peak frequency in an acceleration frequency spectrum, and takes a PPG acceleration peak frequency in a PPG frequency spectrum corresponding to the acceleration peak frequency as a respiration rate.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part application of U.S. patentapplication Ser. No. 15/132,389 filed on, Apr. 19, 2016, which claimsthe priority benefit of Taiwan Patent Application Serial Number104117736, filed on Jun. 1, 2015, the full disclosure of which isincorporated herein by reference.

BACKGROUND 1. Field of the Disclosure

This disclosure generally relates to an optical physiological detectiondevice and a detection method thereof, more particularly, to an opticalrespiration rate detection device using photoplethysmography signals anda detection method thereof.

2. Description of the Related Art

Conventional pulse oximeters utilize a non-invasive method to monitorthe blood oxygenation and the heart rate of a user. A conventional pulseoximeter generally emits a red light beam (wavelength of about 660 nm)and an infrared light beam (wavelength of about 910 nm) to penetrate apart of the human body and detects an intensity variation of thepenetrating light based on the feature that the oxyhemoglobin and thedeoxyhemoglobin have different absorptivities in particular spectrum,e.g. referring to U.S. Pat. No. 7,072,701 and entitled “Method forspectrophotometric blood oxygenation monitoring”. After the intensityvariation of the penetrating light, e.g., photoplethysmography signalsor PPG signals, of the two wavelengths is detected, the bloodoxygenation can be calculated according to an equation OxygenSaturation=100%×[HbO₂]/([HbO₂]+[Hb]), wherein [HbO₂] is an oxyhemoglobinconcentration and [Hb] is a deoxy-hemoglobin concentration.

Generally, the intensity variation of the penetrating light of the twowavelengths detected by a pulse oximeter will increase and decrease withheartbeats. This is because blood vessels will expand and contract withheartbeats such that the blood volume through which the light beams passwill change to accordingly change the ratio of light energy beingabsorbed. Therefore, the heart rate of a user can be calculatedaccording to the PPG signal.

In addition to the above oxygen saturation and the heart rate, the PPGsignal can also be used to measure a respiration rate. However, the PPGsignal generally has ultra low frequency noises which can degrade theaccuracy of the respiration rate measurement.

SUMMARY

Accordingly, the present disclosure provides an optical respiration ratedetection device with high detection accuracy and a detection methodthereof.

The present disclosure provides an optical respiration rate detectiondevice and a detection method thereof that previously categorize arespiration rate range of a current user to remove the noiseinterference thereby improving the detection accuracy.

The present disclosure further provides an optical respiration ratedetection device and a detection method thereof that combine calculationresults of different respiration rate algorithms using differentweightings to improve the detection accuracy.

The present disclosure provides a respiration rate detection deviceincluding an optical sensing unit, an acceleration sensing unit and aprocessing unit. The optical sensing unit is configured to output anintensity variation signal. The acceleration sensing unit is configuredto output an acceleration signal. The processing unit is configured toconvert the intensity variation signal to a first frequency domain dataand convert the acceleration signal to a second frequency domain data,determine a frequency range according to a second peak frequency in thesecond frequency domain data, and calculate a respiration rate accordingto a first peak frequency within the frequency range of the firstfrequency domain data.

The present disclosure provides a respiration rate detection deviceincluding an optical sensing unit, an acceleration sensing unit and aprocessing unit. The optical sensing unit is configured to output anintensity variation signal. The acceleration sensing unit is configuredto output an acceleration signal. The processing unit is configured toconvert the intensity variation signal to a first frequency domain dataand convert the acceleration signal to a second frequency domain data,calculate a second peak value within a predetermined frequency range ofthe second frequency domain data, determine a first peak frequency of afirst peak value in the first frequency domain data corresponding to thesecond peak value, and calculate a respiration rate according to thefirst peak frequency.

The present disclosure provides a respiration rate detection deviceincluding an optical sensing unit, an acceleration sensing unit and aprocessing unit. The optical sensing unit is configured to output anintensity variation signal. The acceleration sensing unit is configuredto output an acceleration signal. The processing unit is configured toconvert the intensity variation signal to a first frequency domain dataand convert the acceleration signal to a second frequency domain data,determine a denoising range according to a second peak frequency in thesecond frequency domain data, remove the first frequency domain datawithin the denoising range of the first frequency domain data, and takea first peak frequency in the remained first frequency domain data as arespiration rate.

The optical respiration rate detection device of the present disclosureis a transmissive detection device or a reflective detection device.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects, advantages, and novel features of the present disclosurewill become more apparent from the following detailed description whentaken in conjunction with the accompanying drawings.

FIG. 1 is a schematic block diagram of a respiration rate detectiondevice according to a first embodiment of the present disclosure.

FIG. 2A is a schematic diagram of an intensity variation signalgenerated by a respiration rate detection device according to anembodiment of the present disclosure.

FIG. 2B is a schematic diagram of frequency domain data generated by arespiration rate detection device according to an embodiment of thepresent disclosure.

FIG. 3 is a flow chart of a respiration rate detection method accordingto a first embodiment of the present disclosure.

FIG. 4 is a schematic block diagram of a respiration rate detectiondevice according to a second embodiment of the present disclosure.

FIG. 5 is a schematic diagram of a look-up table of a respiration ratedetection device according a second embodiment of the presentdisclosure.

FIG. 6 is a flow chart of a respiration rate detection method accordingto a second embodiment of the present disclosure.

FIG. 7 is a schematic block diagram of a respiration rate detectiondevice according to a third embodiment of the present disclosure.

FIG. 8 is an operational schematic diagram of a respiration ratedetection device according to a third embodiment of the presentdisclosure.

DETAILED DESCRIPTION OF THE EMBODIMENT

The illustration below includes embodiments of the present disclosure toclarify how the present disclosure is applied to actual conditions. Itshould be mentioned that elements not directly related to the presentdisclosure are omitted in the drawings. Meanwhile, to clarify therelationship between elements, scales of the element in the drawings maynot be identical to actual scales.

Referring to FIG. 1, it is a schematic block diagram of a respirationrate detection device 100 according to a first embodiment of the presentdisclosure. The respiration rate detection device 100 categorizescurrently detected photoplethysmography signals (or PPG signals)according to predetermined categorization data so as to remove the noiseinterference in a part of frequency zones thereby increasing thedetection accuracy. The respiration rate detection device 100 includes alight source 11, an optical sensing unit 12 and a processing unit 13.

The light source 11 is selected from a coherent light source, apartially coherent light source or a non-coherent light source withoutparticular limitations, e.g., a light emitting diode or a laser diode.The light source 11 provides light to illuminate a skin region SR. Thelight enters skin tissues under the skin region SR and then emerges fromthe skin region SR after propagating inside the skin tissues for adistance. In some embodiments, an illumination wavelength of the lightsource 11 is selected from those used in conventional pulse oximeters.In other embodiments, an illumination wavelength of the light source 11is selected from 300 nm to 940 nm. It should be mentioned that, althoughFIG. 1 shows only one light source 11, it is only intended to illustratebut not to limit the present disclosure. In some embodiments, if therespiration rate detection device 100 is also used for detecting anoxygen saturation, two light sources respectively illuminating red lightand infrared light are used. In other embodiments, if the respirationrate detection device 100 also has a calibration function, three lightsources respectively illuminating green light, red light and infraredlight are used, wherein the green light PPG signal is used to determinea filter parameter for filtering the red light PPG signal and theinfrared light PPG signal.

The optical sensing unit 12 detects light emergent from the skin regionSR and outputs an intensity variation signal. In some embodiments, theoptical sensing unit 12 is a photodiode and the intensity variationsignal outputted from the photodiode is used as the PPG signal. In someembodiments, the optical sensing unit 12 is an image sensor which has apixel array including a plurality of pixels. Each pixel of the pixelarray respectively outputs an intensity signal within a frame and theprocessing unit 13 further calculates a sum of the intensity signalsoutputted from a plurality of pixels within the frame, wherein avariation of the sum of the intensity signals with time is used as thePPG signal. In some embodiments, an intensity variation signal outputtedby each pixel of the pixel array is used as the PPG signal, i.e. thepixel array outputting a plurality of intensity variation signals. Inaddition, in some embodiments when the optical sensing unit 12 is animage sensor, it is preferably an active image sensor, e.g., a CMOSimage sensor. In the active image sensor, a window of interest isdetermined according to an actual intensity distribution detected by thepixel array thereof, wherein the processing unit 13 processes pixel dataonly within the window of interest but ignores pixel data outside thewindow of interest so as to improve the practicability thereof.

The processing unit 13 is, for example, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), amicrocontroller (MCU) or a central processing unit (CPU) for receivingand post-processing the intensity variation signal outputted from theoptical sensing unit 12. In this embodiment, the processing unit 13converts the intensity variation signal to frequency domain data,categorizes the frequency domain data into one of a plurality offrequency zones according to predetermined categorization data, andcalculates a respiration rate according to the frequency domain data ofthe categorized frequency zone.

The processing unit 13 includes, for example, a categorization module131, a PPG measurement module 133, a frequency conversion module 135 anda respiration calculation module 137. It should be mentioned thatalthough FIG. 1 shows functions performed by the processing unit 13 asdifferent functional blocks, it is only intended to illustrate but notto limit the present disclosure. The functions performed by thecategorization module 131, the PPG measurement module 133, the frequencyconversion module 135 and the respiration calculation module 137 are allconsidered to be performed by the processing unit 13 and implemented bysoftware, hardware or a combination thereof without particularlimitations.

Referring to FIGS. 1 and 2A-2B, FIG. 2A is a schematic diagram of anintensity variation signal (or PPG signal) generated by a respirationrate detection device according to an embodiment of the presentdisclosure, and FIG. 2B is a schematic diagram of frequency domain datagenerated by a respiration rate detection device according to anembodiment of the present disclosure.

The PPG measurement module 133 receives the intensity variation signalfrom the optical sensing unit 12 and continuously acquires intensitysignals within a time interval, e.g., 5 to 10 seconds, to be used as thePPG signal. For example, FIG. 2A shows the intensity variation signalwithin a time interval of 6 seconds to be used as the PPG signal. As theoptical sensing unit 12 sequentially outputs intensity signals at asample rate (or frame rate), the time intervals may or may not beoverlapped with one another in time. For example, the PPG measurementmodule 133 takes the intensity variation signal between 1 to 7 secondsas a next PPG signal or takes the intensity variation signal between 7to 13 seconds as a next PPG signal, and so on.

When the optical sensing unit 12 is a photodiode, the PPG measurementmodule 133 directly retrieves the intensity variation signal beingoutputted within a time interval as the PPG signal, wherein the PPGmeasurement module 133 does not perform any processing on the intensityvariation signal or performs the pre-processing such as filtering oramplifying on the intensity variation signal. When the optical sensingunit 12 is an image sensor, the PPG measurement module 133 calculates asum of intensity signals of at least a part of pixel data (e.g. pixeldata within a window of interest) of every frame outputted by the pixelarray, and continuously retrieves the sum of intensity signals within atime interval, e.g., 5 to 10 seconds, as the PPG signal as shown in FIG.2A. In other embodiments, when the optical sensing unit 12 is an imagesensor, the image sensor itself has the function of calculating the sumof intensity signals (e.g., implemented by circuit). In this case, thePPG measurement module 133 retrieves the sum of intensity signals withina time interval, e.g., 5 to 10 seconds, as the PPG signal. In this case,the PPG measurement module 133 does not perform any processing on thesum of intensity signals or performs the pre-processing such asfiltering or amplifying on the sum of intensity signals. It should bementioned that although FIG. 2A shows the intensity variation signalwithin 6 seconds being used as the PPG signal, it is only intended toillustrate but not to limit the present disclosure.

The frequency conversion module 135 converts the intensity variationsignal (or PPG signal) into frequency domain data as shown in FIG. 2B,wherein the frequency conversion is selected from, for example, the fastFourier transform (FFT) or discrete Fourier transform (DFT) withoutparticular limitations.

As shown in FIG. 2B, if there is no ultra low frequency noise, themaximum spectral amplitude should appear at a position Nb1 in thefrequency domain data. However, when ultra low frequency noises exist,another maximum spectral amplitude at a position Nb1′ could exist in thefrequency domain data to lead to a misidentification. Accordingly, thefrequency conversion module 135 further sends the frequency domain datato the categorization module 131 to be compared with predeterminedcategorization data therein. The categorization module 131 categorizesthe frequency domain data as one of a plurality of frequency zones,e.g., an ultra low frequency zone or a low frequency zone shown in FIG.2B. In some embodiments, the categorization module 131 separates twofrequency zones by an isolation frequency, wherein the isolationfrequency is selected from a frequency range between 0.15 Hz and 0.25Hz, but not limited thereto. It is appreciated that when the processingunit 13 separates more than two frequency zones, the isolationfrequencies are selected from more than two frequency ranges.

In the present disclosure, the predetermined categorization data ispreviously built up by a machine learning algorithm, wherein the machinelearning algorithm is implemented by, e.g., the neural network, supportvector machine, random forest and so on without particular limitations.As shown in FIG. 1, a machine learning algorithm unit 15 previouslyreceives a plurality of ultra low frequency learning data Td1 and lowfrequency learning data Td2 for learning so as to recognize datacharacteristics of different frequency zones, wherein the ultra lowfrequency learning data Td1 and the low frequency learning data Td2 arethe frequency domain data obtained from the categorized (e.g.,categorized ultra low frequency data or categorized low frequency data)PPG signal previously converted by the frequency conversion module 135.It is appreciated that when there are more frequency zones to becategorized (i.e. not limited to the ultra low frequency zone or lowfrequency zone), more types of the learning data (i.e. frequency domaindata) are required. It should be mentioned that although FIG. 1 showsthat the machine learning algorithm unit 15 is outside of the processingunit 13, e.g., in an external host or an external computer system, thepresent disclosure is not limited thereto. In other embodiments, themachine learning algorithm unit 15 is included inside the processingunit 13.

Finally, the respiration calculation module 137 calculates a respirationrate Nb1 according to the frequency domain data of the categorizedfrequency zone. For example, the respiration calculation module 137takes a frequency corresponding to a maximum spectral amplitude in thecategorized frequency zone as a respiration frequency (respirationrate). Referring to FIG. 2B, when the categorization module 131categorizes current frequency domain data into the low frequency zone,the respiration calculation module 137 takes a frequency correspondingto the maximum spectral amplitude Nb1 therein as a current respirationrate, which is then outputted; when the categorization module 131categorizes current frequency data as the ultra low frequency zone, therespiration calculation module 137 takes a frequency corresponding tothe maximum spectral amplitude Nb1′ therein as a current respirationrate, which is then outputted.

In this embodiment, the processing unit 13 ignores the frequency domaindata outside the categorized frequency zone. For example, when thefrequency domain data is categorized as the low frequency zone, thefrequency domain data in the ultra low frequency zone is ignored;whereas, when the frequency domain data is categorized as the ultra lowfrequency zone, the frequency domain data in the low frequency zone isignored. In addition, the operation of embodiments having more frequencyzones is similar. It is possible to implement the ignoring as below.

In one embodiment, the frequency conversion module 135 provides currentfrequency domain data to the categorization module 131 to be comparedwith predetermined categorization data therein and categorized. Thecategorization module 131 informs the frequency conversion module 135 ofthe categorized result to allow the frequency conversion module 135 toprovide the frequency domain data only in the categorized frequency zoneto the respiration calculation module 137. Accordingly, the respirationcalculation module 137 will not process the frequency domain dataoutside the categorized frequency zone.

In another embodiment, the frequency conversion module 135 provides allcurrent frequency domain data to the respiration calculation module 137,and the categorization module 131 provides categorization information tothe respiration calculation module 137. Accordingly, when a currentrespiration rate obtained by the respiration calculation module 137 iswithin a categorized frequency zone, the current respiration rate isoutputted; whereas, when the current respiration rate obtained by therespiration calculation module 137 is not within the categorizedfrequency zone, a frequency corresponding to a next maximum spectralamplitude is calculated and confirmed with the categorized frequencyzone till a current respiration rate within the categorized frequencyzone is obtained and the current respiration rate within the categorizedfrequency zone is then outputted. Or the respiration calculation module137 calculates the current respiration rate according to the frequencydomain data only within a categorized frequency zone but ignores thefrequency domain data outside the categorized frequency zone.

Referring to FIG. 3, it is a flow chart of a respiration rate detectionmethod according to a first embodiment of the present disclosureincluding the steps of: providing, by a light source, light toilluminate a skin region (Step S31); detecting, by an optical sensingunit, emergent light from the skin region and outputting an intensityvariation signal (Step S32); converting the intensity variation signalto frequency domain data (Step S33); categorizing the frequency domaindata according to predetermined categorization data (Step S34); andcalculating a respiration rate according to the frequency domain data ofa categorized frequency zone (Step S35). The respiration rate detectionmethod of this embodiment is applicable, for example, to the respirationrate detection device 100 of FIG. 1, and since details of implementationhave been illustrated above, details thereof are not repeated herein.

By using the respiration rate detection device and the respiration ratedetection method of the first embodiment of the present disclosure, theinterference from noises outside the categorized frequency zone isremoved thereby improving the detection accuracy.

Referring to FIG. 4, it is a schematic block diagram of a respirationrate detection device 200 according to a second embodiment of thepresent disclosure. The respiration rate detection device 200 determinesa set of weightings and a set of respiration rate calculation algorithmsaccording to a main frequency amplitude of a current PPG signal, takesrespiration rates obtained by different respiration rate calculationalgorithms as respiration rate components, and combines the respirationrate components according to the set of weightings to form an outputrespiration rate thereby improving the detection accuracy. Therespiration rate detection device 200 includes a light source 21, anoptical sensing unit 22 and a processing unit 23, wherein the lightsource 21 and the optical sensing unit 22 are similar to those of thefirst embodiment and thus details thereof are not repeated herein.

In this embodiment, the processing unit 23 is also selected from adigital signal processor (DSP), a microcontroller (MCU) or a centralprocessing unit (CPU), and used to receive an intensity variation signaloutputted from the optical sensing unit 12 and perform thepost-processing. The processing unit 23 converts the intensity variationsignal into frequency domain data, determines a set of weightings and aset of respiration rate calculation algorithms according to a signal tonoise ratio (SNR) of the frequency domain data, and calculates arespiration rate according to the set of weightings and the set ofrespiration rate calculation algorithms.

The processing unit 23 includes a PPG measurement module 233, afrequency conversion module 235, a weighting determining module 236, arespiration calculation module 237 and a plurality of respiration ratecalculation units 2311 to 231N, wherein the function of the PPGmeasurement module 233 is similar to the PPG measurement module 133 ofthe first embodiment and thus details thereof are not repeated herein.The frequency conversion module 235 converts the PPG signal (e.g., shownin FIG. 2A) outputted by the PPG measurement module 233 into frequencydomain data (e.g., shown in FIG. 2B). It should be mentioned thatalthough FIG. 4 shows functions performed by the processing unit 23 asdifferent functional blocks, it is only intended to illustrate but notto limit the present disclosure. The functions performed by the PPGmeasurement module 233, the frequency conversion module 235, theweighting determining module 236, the respiration calculation module 237and the plurality of respiration rate calculation units 2311 to 231N areall considered to be executed by the processing unit 23 and implementedby software, hardware or a combination thereof without particularlimitations.

In the present disclosure, respiration rate calculation algorithmsinclude, for example, directly performing the Fourier spectrum analysison the PPG signal, acquiring respiration characteristics in the PPGsignal (e.g. characteristics of amplitude variation or frequencyvariation) and then performing the Fourier spectrum analysis on therespiration characteristics, the independent component analysis and theadaptive noise filtering, without particular limitations. Therespiration rate calculation algorithms also include the self-designedrespiration rate calculation algorithm which calculates a currentrespiration rate in time domain or frequency domain. Any respirationrate calculation algorithms are applicable to the respiration ratedetection device 200 as long as different respiration rate calculationalgorithms correspond to different signal features, e.g., the signal tonoise ratio or energy distribution, wherein said different signalfeatures are used to determine the weighting corresponding to theassociated respiration rate calculation algorithm. For example, althougha distortion is not obvious by directly performing the Fourier spectrumanalysis on the PPG signal, the result is easily influenced by ultra lowfrequency noises. Accordingly, when the respiration rate componentobtained by the Fourier spectrum analysis is within an ultra lowfrequency zone, the weighting corresponding to the Fourier spectrumanalysis is reduced so as to reduce the interference from noises withinthe ultra low frequency zone.

In one embodiment, it is assumed that the above four respiration ratecalculation algorithms are used, and the weighting corresponding to eachrespiration rate calculation algorithm is assumed to be 1 at first. If asignal to noise ratio of the obtained frequency domain data is lowerthan a first threshold (e.g., threshold1), it means that the noise isobvious such that the weighting corresponding to the adaptive noisefiltering is increased (e.g., increasing the weighting by 1). If thesignal to noise ratio of the obtained frequency domain data is higherthan a second threshold (e.g., threshold2), it means that the noise isnot obvious such that the weighting corresponding to directly performingthe Fourier spectrum analysis on the PPG signal is increased (e.g.,increasing the weighting by 1). If a sum of spectral amplitudes of ultralow frequency signals (or a ratio of the sum of spectral amplitudes ofultra low frequency signals with respect to a sum of spectral amplitudesof low frequency signals) is higher than a third threshold (e.g.,threshold3), it means that the respiration characteristics in the PPGsignal are easily interfered by ultra low frequency noises such that theweighting corresponding to acquiring respiration characteristics in thePPG signal and then performing the Fourier spectrum analysis on therespiration characteristics is decreased (e.g., decreasing the weightingby 1) and/or the weighting corresponding to the independent componentanalysis is increased (e.g., increasing the weighting by 1). If a sum ofspectral amplitudes of ultra low frequency signals (or a ratio of thesum of spectral amplitudes of ultra low frequency signals with respectto a sum of spectral amplitudes of low frequency signals) is lower thana fourth threshold (e.g., threshold4), the weighting corresponding toacquiring respiration characteristics in the PPG signal and thenperforming the Fourier spectrum analysis on the respirationcharacteristics is increased (e.g., increasing the weighting by 1).

Next, referring to FIGS. 2B, 4-5, FIG. 5 is a schematic diagram of alook-up table of a respiration rate detection device according a secondembodiment of the present disclosure.

The weighting determining module 236 determines a set of weightings anda set of respiration rate calculation algorithms according to a signalto noise ratio (SNR) of the frequency domain data. In some embodiments,the signal to noise ratio is a ratio of a maximum spectral amplitudewith respect to a sum of other spectral amplitudes in the frequencydomain data. For example in FIG. 2B, the signal to noise ratio is aratio of a spectral amplitude at Nb1′ with respect to a sum of otherspectral amplitudes. Accordingly, after the weighting determining module236 obtains a signal to noise ratio, the signal to noise ratio iscompared with a look-up table as shown in FIG. 5, wherein therelationship of a plurality of signal to noise ratios with respect to aplurality of weightings is previously built up to form the look-uptable. In other words, the processing unit 23 is built in a plurality ofrespiration rate calculation algorithms (e.g., 2311 to 231N), and theselected set of respiration rate calculation algorithms includes atleast one of the stored respiration rate calculation algorithms, andeach signal to noise ratio (e.g., SNR₁ to SNR_(N)) corresponds to a setof weightings and an associated set of respiration rate calculationalgorithms. It should be mentioned that although FIG. 5 shows therelationship of a plurality of signal to noise ratios with respect to aplurality of weightings, it is only intended to illustrate but not tolimit the present disclosure. In some embodiments, the look-up tablestores the relationship of a plurality of signal to noise ratio rangeswith respect to a plurality of weightings. In other embodiments, thelook-up table stores the relationship of a plurality of signal to noiseratios (or signal to noise ratio ranges) and frequency zones withrespect to a plurality of weightings. In the present disclosure, theweighting may be between 0 and 1. In other words, when the weightingcorresponding to one respiration rate calculation algorithm is 0, itmeans that the respiration rate calculation algorithm is not used. Inother embodiments, the look-up table stores the relationship of aplurality of energy distributions (e.g., a sum of spectral amplitudes ofultra low frequency signals, a ratio of a sum of spectral amplitudes ofultra low frequency signals with respect to a sum of spectral amplitudesof low frequency signals) with respect to a plurality of weightings.

Finally, the respiration calculation module 237 calculates a respirationrate Nb2 according to the selected set of weightings and the selectedset of respiration rate calculation algorithms. In one embodiment, eachalgorithm of the selected set of respiration rate calculation algorithmsrespectively calculates a respiration rate component R₁, R₂ . . . R_(N)according to the intensity variation signal. For example, therespiration rate Nb2 is a sum of products of each of the selected set ofweightings W₁, W₂ . . . W_(N) and each of the respiration rate componentR₁, R₂ . . . R_(N) obtained by the associated respiration ratecalculation algorithm, i.e. Nb2=R₁×W₁+R₂×W₂+ . . . +R_(N)×W_(N), whereinat least one of R₁, R₂ . . . R_(N) is not zero. In other words,according to actually acquired frequency domain data, it is possiblethat the respiration calculation module 237 calculates a currentrespiration rate according to one respiration rate calculationalgorithm, and in this case the weighting corresponding to the onerespiration rate calculation algorithm is set to 1 and the weightingscorresponding to other respiration rate calculation algorithms are setto zero. That is, the above respiration rate components are therespiration rates obtained by every respiration rate calculationalgorithm, and when a set of respiration rate calculation algorithmsincludes more than one respiration rate calculation algorithms, therespiration rate obtained by each of the more than one respiration ratecalculation algorithms is not directly used as an output respirationrate and referred as a respiration rate component herein. When a set ofrespiration rate calculation algorithms includes one respiration ratecalculation algorithm, the respiration rate component obtained by theone respiration rate calculation algorithm is used as an outputrespiration rate.

Referring to FIG. 6, it is a flow chart of a respiration rate detectionmethod according to a second embodiment of the present disclosureincluding the steps of: providing, by a light source, light toilluminate a skin region (Step S61); detecting, by an optical sensingunit, emergent light from the skin region and outputting an intensityvariation signal (Step S62); converting the intensity variation signalto frequency domain data (Step S63); calculating a signal to noise ratioof the frequency domain data (Step S64); determining a set of weightingsand a set of respiration rate calculation algorithms according to thesignal to noise ratio (Step S65); and calculating a respiration rateaccording to the set of weightings and the set of respiration ratecalculation algorithms (Step S66). The respiration rate detection methodof this embodiment is applicable to the respiration rate detectiondevice 200 of FIG. 4.

Referring to FIGS. 2A-2B and 4-6, details of this embodiment areillustrated hereinafter.

Step S61: The light source 21 emits light of a predetermined opticalspectrum to illuminate a skin region SR. As described in the firstembodiment, corresponding to different applications, it is possible thatthe respiration rate detection device 200 includes more than one lightsource.

Step S62: The optical sensing unit 22 detects emergent light from theskin region SR and outputs an intensity variation signal. As describedin the first embodiment, the optical sensing unit 22 is a light emittingdiode or an image sensor having a pixel array.

Step S63: As described in the first embodiment, the PPG measurementmodule 233 continuously acquires the intensity variation signal within atime interval (e.g., 5 to 10 seconds) to be used as the PPG signal,wherein according to different embodiments of the optical sensing unit22, the intensity variation signal is the intensity signals or a sum ofintensity signals within a time interval. The frequency conversionmodule 235 converts the intensity variation signal (or the PPG signal)into frequency domain data.

Step S64: The weighting determining unit 236 calculates a signal tonoise ratio of the frequency domain data at first. For example, theweighting determining unit 236 determines a main frequency, e.g., Nb1′shown in FIG. 2B having a maximum spectral amplitude and taken as themain frequency, in the frequency domain data at first. Then, theweighting determining unit 236 calculates a ratio of a spectralamplitude of the main frequency with respect to a sum of other spectralamplitudes in the frequency domain data to be used as the signal tonoise ratio herein.

Step S65: Then, the weighting determining unit 236 compares the signalto noise ratio with a look-up table (as shown in FIG. 5) to determine aset of weightings and a set of respiration rate calculation algorithms.As mentioned above, the look-up table previously stores the relationshipof a plurality of signal to noise ratios (or a plurality of signal tonoise ranges) with respect to a plurality of weightings, e.g., storingin a memory of the processing unit 23. Accordingly, when the weightingdetermining unit 236 obtains a signal to noise ratio, a set ofweightings and a set of respiration rate calculation algorithms aredetermined correspondingly.

After the set of respiration rate calculation algorithms is determined,each algorithm of the determined set of respiration rate calculationalgorithms respectively calculates a respiration rate component R₁, R₂ .. . R_(N) according to the intensity variation signal (or the PPGsignal). It is appreciated that the respiration rate calculationalgorithm not included in the selected set of respiration ratecalculation algorithms does not operate so as to reduce the systemresources.

Step S66: Finally, the respiration calculation module 237 calculates asum of products of each of the set of weightings W₁, W₂ . . . W_(N) andeach of the respiration rate components R₁, R₂ . . . R_(N) obtained bythe set of respiration rate calculation algorithms corresponding to theset of weightings, e.g., Nb2=R₁×W₁+R₂×W₂+ . . . +R_(N)×W_(N), and thesum of products Nb2 is then outputted.

In the present disclosure, the respiration rate Nb1 or Nb2 outputted bythe processing unit 13 or 23 is applicable to different applications,e.g., being displayed, being compared with at least one threshold, beingrecorded and so on without particular limitations.

In some embodiments, the respiration rate detection methods in the abovefirst and second embodiments are combinable to further improve thedetection accuracy. For example, the first embodiment is initially usedto remove the frequency domain data in some frequency zones, and thenthe second embodiment is used to calculate the frequency domain databeing left (e.g., the frequency domain data in the ultra low frequencyzone or in the low frequency zone shown in FIG. 2B). Details of the twoembodiments are illustrated above, and thus details thereof are notrepeated herein.

It should be mentioned that although FIGS. 1 and 4 show that the lightsources 11 and 21 and the optical sensing units 12 and 22 are located ata same side of a skin region SR to form a reflective detection device,it is only intended to illustrate but not to limit the presentdisclosure. In other embodiments, the light source and the opticalsensing unit are located at opposite sides of the skin region to form atransmissive detection device.

As mentioned above, the PPG signal sometimes includes ultra lowfrequency noises to degrade the detection accuracy. Accordingly, thepresent disclosure further provides a respiration rate detection devicethat utilizes the detection result of an acceleration sensor to confirma respiration rate.

Referring to FIG. 7, it a schematic block diagram of a respiration ratedetection device 700 according to a third embodiment of the presentdisclosure including an acceleration sensing unit 71, an optical sensingunit 72 and a processing unit 73 electrically coupled to the two sensingunits. The respiration rate detection device 700 further includes alight source as mentioned above, and thus details thereof are notrepeated herein.

The respiration rate obtained by the respiration rate detection device700 is transmitted to a display 75 via a wired or wireless communicationinterface (not shown in FIG. 7) to be shown thereon using numbers orgraphs. The display 75 is included in or separated from the respirationrate detection device 700.

The optical sensing unit 72 of this embodiment is identical to theoptical sensing unit 12 shown in FIG. 1 and also used to output anintensity variation signal (or referred to PPG signal). The opticalsensing unit 72 is a photodiode or a CMOS image sensor as mentionedabove, and thus details thereof are not repeated herein.

The acceleration sensing unit 71 is, for example, a MEMS accelerationsensor, a G-sensor or the like, and is used to output an accelerationsignal ACC. The acceleration sensing unit 71 is preferably arrangedclose to or adjacent to the optical sensing unit 72 and operatessimultaneously with the optical sensing unit 72. More specifically,within a detection time-interval, the optical sensing unit 72 outputs atime-varied intensity variation signal PPG and the acceleration sensingunit 71 outputs a time-varied acceleration signal ACC to the processingunit 73 for the post-processing, e.g., filtering, comparing andcalculation.

Similar to the processing unit 13, the processing unit 73 is also anASIC or a DSP that performs the calculation using a hardware and/orsoftware. The processing unit 73 also includes a PPG frequencyconversion module 732 (operating similar to 135) used to convert theintensity variation signal PPG to a first frequency domain data, e.g.,the PPG spectrum S_(PPG) shown in FIG. 8. The processing unit 73 furtherincludes an acceleration (ACC) frequency conversion module 731 used toconvert the acceleration signal ACC to a second frequency domain data,e.g., the ACC spectrum S_(ACC) shown in FIG. 8.

FIG. 8 is an operational schematic diagram of a respiration ratedetection device 700 according to a third embodiment of the presentdisclosure. The processing unit 73 further includes a respirationcalculation circuit 733 used to confirm a correct respiration rate andeliminate noises according to the first frequency domain data S_(PPG)and the second frequency domain data S_(ACC). It is appreciated thatalthough FIG. 7 shows three separated blocks, operations of these threeseparated blocks are all considered executed by the processing unit 73.

In one aspect, the processing unit 73 is used to determine a frequencyrange according to a second peak frequency (e.g., a frequencycorresponding to Na1 shown in FIG. 8) in the second frequency domaindata S_(ACC), and calculate a respiration rate according to a first peakfrequency (e.g., a frequency corresponding to Nb1 shown in FIG. 8)within the determined frequency range of the first frequency domain dataS_(PPG). As noises in the PPG signal contain a double frequency and ahalf frequency of the respiration rate, the frequency range ispreferably set as a range of 0.1 Hz taking the second peak frequency Na1as a center thereof. If the respiration rate of a user changes, thesecond peak frequency and the corresponding frequency range are alsochanged.

In another aspect, the processing unit 73 is used to calculate a secondpeak value (e.g., Na1) within a predetermined frequency range of thesecond frequency domain data S_(ACC), and determine a first peakfrequency of a first peak value (e.g., Nb1) in the first frequencydomain data S_(PPG) corresponding to the second peak value Na1. Finally,the processing unit 73 calculates a respiration rate according to thefirst peak frequency.

As mentioned above, as the respiration rate is within a predeterminedrange, the predetermined frequency range is preferably set between 0.1Hz and 0.5 Hz, or set adaptably corresponding to different usersmanually or automatically.

In this aspect, the first peak value Nb1 corresponding to the secondpeak value Na1 is, for example, referred to that (i) the two peaks Na1and Nb1 have a frequency difference Δf smaller than 0.05 Hz, (ii) theoverlapping of a full-width at half maximum (FWHM) of a spectrum shapeof the second peak with that of the first peak is larger than apredetermined threshold or a predetermined ratio, or (iii) select one ofmultiple peak values in the first frequency domain data S_(PPG) closestto the second peak value Na1, e.g., Nb1 instead of Nb′.

In another aspect, the processing unit 73 is used to determine adenoising range according to a second peak frequency (e.g., thefrequency corresponding to Na1) in the second frequency domain dataS_(ACC), and remove the first frequency domain data of the firstfrequency domain data S_(PPG) within the determined denoising range.Finally, the processing unit 73 takes a first peak frequency in theremained (i.e. out of the denoising range) first frequency domain dataas a respiration rate.

As mentioned above, some noises in the first frequency domain dataS_(PPG) come from a double frequency and a half frequency of therespiration rate. Accordingly, the denoising range is preferably set asa frequency range outside 0.05 Hz from the second peak frequency, e.g.,0.1 Hz to (Na1−0.05 Hz) and (Na1+0.05 Hz) to 0.5 Hz. The second peakfrequency Na1 is arranged as a center of the denoising range. Forexample, the processing unit 73 removes the first frequency domain datawithin the denoising range from a memory or a buffer. In the case thatthe remained first frequency domain data does not have a peak value, theprocessor 37 increases the denoising range (e.g., from 0.05 Hz to 0.1 Hzeach side) in a next intensity variation signal PPG to keep more firstfrequency domain data.

To further reduce the memory space and increase the processing speed,within a predetermined time interval after the denoising ranged isdetermined, the processing unit 73 further performs a discrete Fouriertransform (DFT) on the intensity variation signal PPG within a remained(out of the denoising range) frequency range without converting the datausing the indices within the denoising range.

When using the respiration rate detection device 700 of the thirdembodiment to measure a respiration rate, the user is preferably in asteady status to obtain a clear second frequency domain data S_(ACC). Inaddition, to eliminate the noises caused by slight motion (e.g. fingermovement) of the user during measurement, the processing unit 73 isfurther used to ignore acceleration data in the acceleration signal ACCassociated with an acceleration value or a slope of accelerationvariation (in the time domain) larger than a predetermined threshold,the acceleration value is too high or the slope is too large. This isbecause that the acceleration, which is caused by the user's breathing,measured by the acceleration sensing unit 71 generally have smoothingacceleration values instead of abruptly changed values.

In the third embodiment, if the acceleration sensing unit 71 does notdetect a clear acceleration peak, e.g., the second frequency domain dataS_(ACC) not containing any second peak value larger than an amplitudethreshold TH, the second frequency domain data S_(ACC) containing morethan one peak larger than an amplitude threshold TH or the SNR thereofbeing too high, the processing unit 73 calculates the respiration rateaccording to the method in the first embodiment or the secondembodiment. For example, the processing unit 73 categorizes the firstfrequency domain data S_(PPG) as one of a plurality of frequency zonesaccording to predetermined categorization data, and calculates arespiration rate using the first frequency domain data S_(PPG) withinthe categorized frequency zone. Or, the processing unit 73 determines aset of weightings associated with a set of respiration rate calculationalgorithms according to a sum of spectral amplitudes of the firstfrequency domain data S_(PPG) lower than a predetermined frequency(e.g., 0.25 Hz, but not limited to), and calculates a respiration rateaccording to the set of weightings and the set of respiration ratecalculation algorithms. Details thereof have been illustrated above, andthus are not repeated herein.

Furthermore, since the respiration rate generally does not exceed a ragebetween 0.1 Hz and 0.5 Hz, the processing unit 37 further firstlyfilters the first frequency domain data S_(PPG) and the second frequencydomain data S_(ACC) using a digital filter having a pass band between0.1 Hz and 0.5 Hz after obtaining the first frequency domain dataS_(PPG) and the second frequency domain data S_(ACC), and thencalculates the respiration rate using the above methods so as to improvethe accuracy.

In the present disclosure, the peak value is referred to maximumspectral amplitude within the concerned frequency spectrum.

As mentioned above, the calculation of a respiration rate using PPGsignals can be influenced by ultra low frequency noises to degrade thedetection accuracy. Therefore, the present disclosure further provides arespiration rate detection device (FIGS. 1 and 4) and a respiration ratedetection method (FIGS. 3 and 6) that improve the detection accuracy bythe previous categorization or combining calculation results ofdifferent algorithms.

Although the disclosure has been explained in relation to its preferredembodiment, it is not used to limit the disclosure. It is to beunderstood that many other possible modifications and variations can bemade by those skilled in the art without departing from the spirit andscope of the disclosure as hereinafter claimed.

What is claimed is:
 1. A respiration rate detection device, comprising:an optical sensing unit configured to output an intensity variationsignal; an acceleration sensing unit configured to output anacceleration signal; and a processing unit configured to convert theintensity variation signal to a first frequency domain data and convertthe acceleration signal to a second frequency domain data, determine afrequency range according to a second peak frequency in the secondfrequency domain data, and calculate a respiration rate according to afirst peak frequency within the frequency range of the first frequencydomain data.
 2. The respiration rate detection device as claimed inclaim 1, wherein the frequency range is a range of 0.1 Hz taking thesecond peak frequency as a center thereof.
 3. The respiration ratedetection device as claimed in claim 1, wherein the processing unit isfurther configured to ignore acceleration data in the accelerationsignal associated with an acceleration value or a slope of accelerationvariation larger than a predetermined threshold.
 4. The respiration ratedetection device as claimed in claim 1, wherein the optical sensing unitis a photodiode or a CMOS image sensor; and the acceleration sensingunit is a MEMS acceleration sensor.
 5. The respiration rate detectiondevice as claimed in claim 1, wherein the processing unit is furtherconfigured to filter the first frequency domain data and the secondfrequency data with a filter having a pass band between 0.1 HZ and 0.5HZ.
 6. The respiration rate detection device as claimed in claim 1,wherein when the second frequency domain data does not contain anysecond peak value larger than a frequency threshold, the processing unitis further configured to categorize the first frequency domain data asone of a plurality of frequency zones according to predeterminedcategorization data, and calculate the respiration rate using the firstfrequency domain data within the categorized frequency zone.
 7. Therespiration rate detection device as claimed in claim 1, wherein whenthe second frequency domain data does not contain any second peak valuelarger than a frequency threshold, the processing unit is furtherconfigured to determine a set of weightings associated with a set ofrespiration rate calculation algorithms according to a sum of spectralamplitudes of the first frequency domain data lower than a predeterminedfrequency, and calculate the respiration rate according to the set ofweightings and the set of respiration rate calculation algorithms.
 8. Arespiration rate detection device, comprising: an optical sensing unitconfigured to output an intensity variation signal; an accelerationsensing unit configured to output an acceleration signal; and aprocessing unit configured to convert the intensity variation signal toa first frequency domain data and convert the acceleration signal to asecond frequency domain data, calculate a second peak value within apredetermined frequency range of the second frequency domain data,determine a first peak frequency of a first peak value in the firstfrequency domain data corresponding to the second peak value, andcalculate a respiration rate according to the first peak frequency. 9.The respiration rate detection device as claimed in claim 8, wherein thepredetermined frequency range is between 0.1 Hz and 0.5 Hz.
 10. Therespiration rate detection device as claimed in claim 8, wherein theprocessing unit is further configured to ignore acceleration data in theacceleration signal associated with an acceleration value or a slope ofacceleration variation larger than a predetermined threshold.
 11. Therespiration rate detection device in claim 8, wherein the correspondingis referred to that overlapping of a full-width at half maximum of aspectrum shape of the second peak value with that of the first peakvalue is larger than a predetermined threshold.
 12. The respiration ratedetection device as claimed in claim 8, wherein when the secondfrequency domain data does not contain any second peak value larger thana frequency threshold, the processing unit is further configured tocategorize the first frequency domain data as one of a plurality offrequency zones according to predetermined categorization data, andcalculate the respiration rate using the first frequency domain datawithin the categorized frequency zone.
 13. The respiration ratedetection device as claimed in claim 8, wherein when the secondfrequency domain data does not contain any second peak value larger thana frequency threshold, the processing unit is further configured todetermine a set of weightings associated with a set of respiration ratecalculation algorithms according to a sum of spectral amplitudes of thefirst frequency domain data lower than a predetermined frequency, andcalculate the respiration rate according to the set of weightings andthe set of respiration rate calculation algorithms.
 14. A respirationrate detection device, comprising: an optical sensing unit configured tooutput an intensity variation signal; an acceleration sensing unitconfigured to output an acceleration signal; and a processing unitconfigured to convert the intensity variation signal to a firstfrequency domain data and convert the acceleration signal to a secondfrequency domain data, determine a denoising range according to a secondpeak frequency in the second frequency domain data, remove the firstfrequency domain data of the first frequency domain data within thedenoising range, and take a first peak frequency in the remained firstfrequency domain data as a respiration rate.
 15. The respiration ratedetection device as claimed in claim 14, wherein the denoising range isa frequency range outside 0.05 Hz from the second peak frequency, whichis taken as a center of the denoising range.
 16. The respiration ratedetection device as claimed in claim 14, wherein the processing unit isfurther configured to ignore acceleration data in the accelerationsignal associated with an acceleration value or a slope of accelerationvariation larger than a predetermined threshold.
 17. The respirationrate detection device as claimed in claim 14, wherein within apredetermined time interval after the denoising ranged is determined,the processing unit is further configured to perform a discrete Fouriertransform on the acceleration signal within a remained frequency range.18. The respiration rate detection device as claimed in claim 14,wherein the processing unit further configured to filter the firstfrequency domain data and the second frequency data with a filter havinga pass band between 0.1 HZ and 0.5 HZ.
 19. The respiration ratedetection device as claimed in claim 14, wherein when the secondfrequency domain data does not contain any second peak value larger thana frequency threshold, the processing unit is further configured tocategorize the first frequency domain data as one of a plurality offrequency zones according to predetermined categorization data, andcalculate the respiration rate using the first frequency domain datawithin the categorized frequency zone.
 20. The respiration ratedetection device as claimed in claim 14, wherein when the secondfrequency domain data does not contain any second peak value larger thana frequency threshold, the processing unit is further configured todetermine a set of weightings associated with a set of respiration ratecalculation algorithms according to a sum of spectral amplitudes of thefirst frequency domain data lower than a predetermined frequency, andcalculate the respiration rate according to the set of weightings andthe set of respiration rate calculation algorithms.